



'''simple docstring'''

from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
			from fastapi import Body, FastAPI, HTTPException
			from fastapi.routing import APIRoute
			from pydantic import BaseModel
			from starlette.responses import JSONResponse
			from uvicorn import run

			SCREAMING_SNAKE_CASE__     :						int	=  True
except (ImportError, AttributeError):
			SCREAMING_SNAKE_CASE__     :						Tuple	=  object






			def 				a  (     *UpperCamelCase_    :    Union[str, Any]      ,					**UpperCamelCase_    :    Optional[Any]			)       ->	Union[str, Any]:
					pass


			SCREAMING_SNAKE_CASE__     :						List[str]	=  False


SCREAMING_SNAKE_CASE__     :						Tuple	=  logging.get_logger('''transformers-cli/serving''')






def 				a  (     UpperCamelCase_    :    Namespace			)       ->	Tuple:
		snake_case__	     =pipeline(
		    task=args.task      ,					model=args.model if args.model else None      ,					config=args.config      ,					tokenizer=args.tokenizer      ,					device=args.device      ,					)
		return ServeCommand(UpperCamelCase_      ,					args.host      ,					args.port      ,					args.workers			)

class 							a__( snake_case__     ):
			a_	: dict





class 							a__( snake_case__     ):
			a_	: List[str]
			a_	: Optional[List[int]]





class 							a__( snake_case__     ):
			a_	: str





class 							a__( snake_case__     ):
			a_	: Any





class 							a__( snake_case__     ):






			@staticmethod
			def 						_lowercase  (     _UpperCAmelCase						)							->							Dict:
					snake_case__	     =parser.add_parser(
					    'serve'					,   help='CLI tool to run inference requests through REST and GraphQL endpoints.'						)
					serve_parser.add_argument(
					    '--task'					,   type=_UpperCAmelCase					,   choices=get_supported_tasks()					,   help='The task to run the pipeline on'					,   )
					serve_parser.add_argument('--host'					,   type=_UpperCAmelCase					,   default='localhost'					,   help='Interface the server will listen on.'						)
					serve_parser.add_argument('--port'					,   type=_UpperCAmelCase					,   default=8888					,   help='Port the serving will listen to.'						)
					serve_parser.add_argument('--workers'					,   type=_UpperCAmelCase					,   default=1					,   help='Number of http workers'						)
					serve_parser.add_argument('--model'					,   type=_UpperCAmelCase					,   help='Model\'s name or path to stored model.'						)
					serve_parser.add_argument('--config'					,   type=_UpperCAmelCase					,   help='Model\'s config name or path to stored model.'						)
					serve_parser.add_argument('--tokenizer'					,   type=_UpperCAmelCase					,   help='Tokenizer name to use.'						)
					serve_parser.add_argument(
					    '--device'					,   type=_UpperCAmelCase					,   default=-1					,   help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'					,   )
					serve_parser.set_defaults(func=_UpperCAmelCase						)






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							int:
					snake_case__	     =pipeline

					snake_case__	     =host
					snake_case__	     =port
					snake_case__	     =workers

					if not _serve_dependencies_installed:
							raise RuntimeError(
							    'Using serve command requires FastAPI and uvicorn. '
							    'Please install transformers with [serving]: pip install "transformers[serving]".'
							    'Or install FastAPI and uvicorn separately.'						)
					else:
							logger.info(f"""Serving model over {host}:{port}"""						)
							snake_case__	     =FastAPI(
							    routes=[
							        APIRoute(
							            '/'					,   self.model_info					,   response_model=_UpperCAmelCase					,   response_class=_UpperCAmelCase					,   methods=['GET']					,   ),
							        APIRoute(
							            '/tokenize'					,   self.tokenize					,   response_model=_UpperCAmelCase					,   response_class=_UpperCAmelCase					,   methods=['POST']					,   ),
							        APIRoute(
							            '/detokenize'					,   self.detokenize					,   response_model=_UpperCAmelCase					,   response_class=_UpperCAmelCase					,   methods=['POST']					,   ),
							        APIRoute(
							            '/forward'					,   self.forward					,   response_model=_UpperCAmelCase					,   response_class=_UpperCAmelCase					,   methods=['POST']					,   ),
							    ]					,   timeout=600					,   )






			def 						_lowercase  (     self						)							->							Optional[Any]:
					run(self._app					,   host=self.host					,   port=self.port					,   workers=self.workers						)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return ServeModelInfoResult(infos=vars(self._pipeline.model.config						)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase = Body(_UpperCAmelCase					,   embed=_UpperCAmelCase						)					,   _UpperCAmelCase = Body(_UpperCAmelCase					,   embed=_UpperCAmelCase						)						)							->							Dict:
					try:
							snake_case__	     =self._pipeline.tokenizer.tokenize(_UpperCAmelCase						)

							if return_ids:
									snake_case__	     =self._pipeline.tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
									return ServeTokenizeResult(tokens=_UpperCAmelCase					,   tokens_ids=_UpperCAmelCase						)
							else:
									return ServeTokenizeResult(tokens=_UpperCAmelCase						)

					except Exception as e:
							raise HTTPException(status_code=500					,   detail={'model': '', 'error': str(_UpperCAmelCase						)}						)






			def 						_lowercase  (     self					,   _UpperCAmelCase = Body(_UpperCAmelCase					,   embed=_UpperCAmelCase						)					,   _UpperCAmelCase = Body(_UpperCAmelCase					,   embed=_UpperCAmelCase						)					,   _UpperCAmelCase = Body(_UpperCAmelCase					,   embed=_UpperCAmelCase						)					,   )							->							Tuple:
					try:
							snake_case__	     =self._pipeline.tokenizer.decode(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)
							return ServeDeTokenizeResult(model=''					,   text=_UpperCAmelCase						)
					except Exception as e:
							raise HTTPException(status_code=500					,   detail={'model': '', 'error': str(_UpperCAmelCase						)}						)






			async def 						_lowercase  (     self					,   _UpperCAmelCase=Body(_UpperCAmelCase					,   embed=_UpperCAmelCase						)						)							->							str:

					# Check we don't have empty string
					if len(_UpperCAmelCase						) == 0:
							return ServeForwardResult(output=[]					,   attention=[]						)

					try:
							# Forward through the model
							snake_case__	     =self._pipeline(_UpperCAmelCase						)
							return ServeForwardResult(output=_UpperCAmelCase						)
					except Exception as e:
							raise HTTPException(500					,   {'error': str(_UpperCAmelCase						)}						)









'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =os.path.join(args.tf_model_dir      ,					'parameters.json'			)
		snake_case__	     =json.loads(open(UpperCamelCase_			).read()			)
		if not params:
				raise ValueError(
				    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""			)
		if not args.output.endswith('.pt'			):
				snake_case__	     =args.output + '.pt'
		snake_case__	     =OrderedDict()
		with tf.device('/CPU:0'			):
				snake_case__	     =tf.train.load_checkpoint(args.tf_model_dir			)
				snake_case__	     =reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						snake_case__	     =reader.get_tensor(UpperCamelCase_			).astype(np.floataa			)
						if key_name.endswith('/adam_m'			) or key_name.endswith('/adam_v'			):
								continue
						if key_name.startswith('pasts/'			):
								if key_name.startswith('pasts/mlp'			):
										snake_case__	     =int(key_name[9]			)
								elif key_name.startswith('pasts/out'			):
										snake_case__	     =8
								snake_case__	     ='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/moe'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/switch_gating/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/softmlp/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/wo/kernel'			) or key_name.endswith('/wi/kernel'			):
										snake_case__	     =key_name[-9:-7]
										for i in range(16			):
												snake_case__	     ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
												snake_case__	     =(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/mlp'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/p1/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p1/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/ln'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/att'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/qkv/kernel'			):
										snake_case__	     =vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										snake_case__	     =state[:, 0, :, :]
										snake_case__	     =state[:, 1, :, :]
										snake_case__	     =state[:, 2, :, :]
										snake_case__	     =(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/o/kernel'			):
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
										snake_case__	     =(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/an'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif (
						    key_name.startswith('model/wte'			)
						    or key_name.startswith('model/wpe'			)
						    or key_name.startswith('model/ete'			)
						):
								snake_case__	     ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
								    key_name[-3:]
								]
								snake_case__	     ='model.%s.weight' % nlayer
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =torch.tensor(UpperCamelCase_			)
								if key_name.startswith('model/wte'			):
										snake_case__	     ='lm_head.weight'
										snake_case__	     =vnp.copy()  # same in embedded
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/wob'			):
								snake_case__	     ='final_logits_bias'
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =state.reshape((1, -1)			)
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense/kernel":
								snake_case__	     ='model.last_project.weight'
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense_1/bias":
								snake_case__	     ='model.last_project.bias'
								snake_case__	     =vnp.copy()  # same because it is one dimensional
								snake_case__	     =torch.tensor(UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					args.output			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)




